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Geospatial analysis of cesarean section in Iran (2016–2020): exploring clustered patterns and measuring spatial interactions of available health services
BACKGROUND: The lives of babies and mothers are at risk due to the uneven distribution of healthcare facilities required for emergency cesarean sections (CS). However, CS without medical indications might cause complications for mothers and babies, which is a global health problem. Identifying spati...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9302231/ https://www.ncbi.nlm.nih.gov/pubmed/35864462 http://dx.doi.org/10.1186/s12884-022-04856-z |
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author | Mohammadi, Alireza Pishgar, Elahe Salari, Zahra Kiani, Behzad |
author_facet | Mohammadi, Alireza Pishgar, Elahe Salari, Zahra Kiani, Behzad |
author_sort | Mohammadi, Alireza |
collection | PubMed |
description | BACKGROUND: The lives of babies and mothers are at risk due to the uneven distribution of healthcare facilities required for emergency cesarean sections (CS). However, CS without medical indications might cause complications for mothers and babies, which is a global health problem. Identifying spatiotemporal variations of CS rates in each geographical area could provide helpful information to understand the status of using CS services. METHODS: This cross-sectional study explored spatiotemporal patterns of CS in northeast Iran from 2016 to 2020. Space–time scan statistics and spatial interaction analysis were conducted using geographical information systems to visualize and explore patterns of CS services. RESULTS: The temporal analysis identified 2017 and 2018 as the statistically significant high clustered times in terms of CS rate. Five purely spatial clusters were identified that were distributed heterogeneously in the study region and included 14 counties. The spatiotemporal analysis identified four clusters that included 13 counties as high-rate areas in different periods. According to spatial interaction analysis, there was a solid spatial concentration of hospital facilities in the political center of the study area. Moreover, a high degree of inequity was observed in spatial accessibility to CS hospitals in the study area. CONCLUSIONS: CS Spatiotemporal clusters in the study area reveal that CS use in different counties among women of childbearing age is significantly different in terms of location and time. This difference might be studied in future research to identify any overutilization of CS or lack of appropriate CS in clustered counties, as both put women at risk. Hospital capacity and distance from population centers to hospitals might play an essential role in CS rate variations and spatial interactions among people and CS facilities. As a result, some healthcare strategies, e.g., building new hospitals and empowering the existing local hospitals to perform CS in areas out of service, might be developed to decline spatial inequity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12884-022-04856-z. |
format | Online Article Text |
id | pubmed-9302231 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-93022312022-07-22 Geospatial analysis of cesarean section in Iran (2016–2020): exploring clustered patterns and measuring spatial interactions of available health services Mohammadi, Alireza Pishgar, Elahe Salari, Zahra Kiani, Behzad BMC Pregnancy Childbirth Research BACKGROUND: The lives of babies and mothers are at risk due to the uneven distribution of healthcare facilities required for emergency cesarean sections (CS). However, CS without medical indications might cause complications for mothers and babies, which is a global health problem. Identifying spatiotemporal variations of CS rates in each geographical area could provide helpful information to understand the status of using CS services. METHODS: This cross-sectional study explored spatiotemporal patterns of CS in northeast Iran from 2016 to 2020. Space–time scan statistics and spatial interaction analysis were conducted using geographical information systems to visualize and explore patterns of CS services. RESULTS: The temporal analysis identified 2017 and 2018 as the statistically significant high clustered times in terms of CS rate. Five purely spatial clusters were identified that were distributed heterogeneously in the study region and included 14 counties. The spatiotemporal analysis identified four clusters that included 13 counties as high-rate areas in different periods. According to spatial interaction analysis, there was a solid spatial concentration of hospital facilities in the political center of the study area. Moreover, a high degree of inequity was observed in spatial accessibility to CS hospitals in the study area. CONCLUSIONS: CS Spatiotemporal clusters in the study area reveal that CS use in different counties among women of childbearing age is significantly different in terms of location and time. This difference might be studied in future research to identify any overutilization of CS or lack of appropriate CS in clustered counties, as both put women at risk. Hospital capacity and distance from population centers to hospitals might play an essential role in CS rate variations and spatial interactions among people and CS facilities. As a result, some healthcare strategies, e.g., building new hospitals and empowering the existing local hospitals to perform CS in areas out of service, might be developed to decline spatial inequity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12884-022-04856-z. BioMed Central 2022-07-21 /pmc/articles/PMC9302231/ /pubmed/35864462 http://dx.doi.org/10.1186/s12884-022-04856-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Mohammadi, Alireza Pishgar, Elahe Salari, Zahra Kiani, Behzad Geospatial analysis of cesarean section in Iran (2016–2020): exploring clustered patterns and measuring spatial interactions of available health services |
title | Geospatial analysis of cesarean section in Iran (2016–2020): exploring clustered patterns and measuring spatial interactions of available health services |
title_full | Geospatial analysis of cesarean section in Iran (2016–2020): exploring clustered patterns and measuring spatial interactions of available health services |
title_fullStr | Geospatial analysis of cesarean section in Iran (2016–2020): exploring clustered patterns and measuring spatial interactions of available health services |
title_full_unstemmed | Geospatial analysis of cesarean section in Iran (2016–2020): exploring clustered patterns and measuring spatial interactions of available health services |
title_short | Geospatial analysis of cesarean section in Iran (2016–2020): exploring clustered patterns and measuring spatial interactions of available health services |
title_sort | geospatial analysis of cesarean section in iran (2016–2020): exploring clustered patterns and measuring spatial interactions of available health services |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9302231/ https://www.ncbi.nlm.nih.gov/pubmed/35864462 http://dx.doi.org/10.1186/s12884-022-04856-z |
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